We follow Uddin et al. [1]} to collect SO posts related to IoT discussions. First, we download the SO data dump [2]}
of September 2019. The SO dataset includes all posts for 11 years between 2008 and September 2019. In total, it has 46,767,375 questions and answers.
Out of those, around 40% are questions and 60% are answers.
Out of the answers, around 34% are accepted. Second, we determine the list of potential IoT security tags in SO, following Yang et al. [3]}. We identify three most popular IoT related tags, i.e., `iot', `arduino', and `raspberry-pi' in SO. As these three have the maximum number of posts in IoT domains, our intuition is that these tags should cover the widest range of security-related topics. Thus, we select these three tags as our primary tags. We denote those as \(\mathcal {T}_{init}\) . Then we collect candidate tags \(\mathcal {T_A}\)  related to \(\mathcal {T}_{init}\)  by analyzing tags that are labeled at least one of those tags \(\mathcal {T}_{init}\) . As this step includes all IoT tags, it makes sure that all security related tags are also added for the study. Following previous research [3]}, [5]},
we systematically filter out irrelevant tags and
finalize a list of all potential IoT tags \(\mathcal {T}\)  as follows. For each tag \(t\)  in \(\mathcal {T_A}\) , we compute its significance and relevance as follows.
\(\textrm {Significance}~\mu (t) = \frac{\# \textrm {of Questions with tag t in \mathcal {P}}}{\# \textrm {of Questions with tag t in SO dump}}\) 
\(\textrm {Relevance}~\nu (t) = \frac{\# \textrm {of Questions with tag t in \mathcal {P}}}{\# \textrm {of Questions in \mathcal {P}}}\) 
